2345 results about "Semantic analytics" patented technology

The invention relates to the technical field of data processing, in particular to an intelligent document retrieval and generation system based on metadata driving, which comprises a metadata extraction module, a dynamic index construction module, an intelligent routing module, a distributed retrieval module and a result generation module. The metadata extraction module extracts organization names, timestamps and technical classification tags from an input document, maps the organization names, the timestamps and the technical classification tags into structured key value pairs, and transmits the structured key value pairs to the dynamic index construction module through a streaming pipeline; and the dynamic index construction module constructs a skip list index based on discrete fields to realize rapid matching, fuses metadata features and document semantics by adopting a multi-head attention mechanism, and outputs a structured response through topic clustering. According to the method, through a metadata pre-filtering, path optimization and semantic analysis cooperation mechanism, the multi-dimensional composite query efficiency in a distributed environment is improved, and noise interference is suppressed.

The invention relates to the technical field of archive management, in particular to an archive full life cycle management system and method based on cloud computing. The method specifically comprises an intelligent classification module based on a format adaptation degree function and semantic density analysis, a five-stage life cycle management module, and an authority scheduling module fusing RBAC / ABAC authority control and block chain auditing, and based on a matrix decomposition archiving static coding mechanism, document security storage and analysis are realized through an orthogonal matrix and pseudo-inverse operation, and the file security is realized. The data security in a long-term archiving scene is improved; cloud storage resources are dynamically optimized according to the document life cycle state and the access frequency, and the storage cost is reduced in combination with a cold and hot layering strategy; and an AI algorithm is introduced, the archive retention value is automatically evaluated according to laws and regulations and semantic analysis, and a compliant destruction decision is realized. According to the method, through multi-model fusion metadata extraction, dynamic strategy binding and automatic state transition, the whole-process management efficiency and safety are remarkably improved.

The invention relates to the technical field of artificial intelligence and intelligent education, and provides a mathematical teaching knowledge graph generation method and system based on artificial intelligence, which are used for optimizing and improving a teaching knowledge graph generation technology to realize more intelligent and accurate teaching knowledge graph generation, and the method comprises the following steps: obtaining a multi-source teaching data set; extracting a mathematical knowledge point entity set from the textbook text data, and generating an association relationship set among mathematical knowledge point entities in the mathematical knowledge point entity set according to the test question structure data; performing hierarchical classification processing on the mathematical knowledge point entity set based on a preset semantic analysis model to obtain knowledge point hierarchical structure data, and calculating weight distribution data of the mathematical knowledge point entities based on the association relationship set; and generating a dynamic knowledge graph topological structure according to the knowledge point hierarchical structure data and the weight distribution data, wherein nodes in the dynamic knowledge graph topological structure comprise semantic vectors and association strength parameters of mathematical knowledge point entities.

The invention relates to a multi-data standard definition conflict resolution method based on a large model and a knowledge graph, and belongs to the field of public data governance and data standardization. The method comprises the following steps: extracting and preprocessing a multi-data standard definition; defining semantic representation and large model analysis: performing semantic analysis on the definition of each data item by using a large model, and generating a semantic embedding vector and a key feature; knowledge graph construction and entity alignment: constructing a knowledge graph containing all data items, and connecting data item nodes with similar semantics in different standards by adopting an entity alignment algorithm to form a candidate alignment relationship; conflict detection and type identification: aiming at the aligned data item definition, comparing attributes and values, identifying definition conflict points, and classifying and marking conflict types; and carrying out conflict resolution and unified definition generation by using a globally optimized conflict resolution algorithm. According to the method, the problems of data islands and semantic conflicts caused by inconsistent data standard definitions in the prior art are solved.

The invention provides a compliance auditing method and system for multi-modal content. The method comprises the following steps: performing feature extraction on unstructured to-be-audited multi-modal content to obtain a structured feature vector; performing image-text semantic association on the text semantic feature vector and the image visual feature vector to obtain a fusion feature vector involving image-text semantic contradiction; constructing a domain knowledge graph based on the compliance guidance data of the domain to which the to-be-audited multi-modal content belongs; inputting the fusion feature vector into a domain knowledge graph, and performing compliance rule retrieval by adopting a sub-graph matching algorithm to determine a violation type corresponding to the fusion feature vector and a violated compliance term; and generating an interactive compliance audit report. The system comprises functional modules for realizing the steps in a one-to-one correspondence manner. According to the technical scheme, the problem that cross-modal semantic analysis of an existing multi-modal content compliance auditing method is not accurate can be solved.

The invention relates to a 5G network slice dynamic scheduling method and system based on multi-modal space-time perception and an event knowledge graph, and belongs to the technical field of mobile communication network resource management. According to the method, the change of a physical scene is sensed in real time by constructing a dynamically evolved event knowledge graph and designing a double-flow space-time cross network in combination with visual semantic analysis; dynamically adjusting the resource prediction model by adopting an event-scene dual-drive mechanism, dynamically adjusting parameters of the gated recurrent neural network through an elastic adjustment factor, and optimizing a multi-target resource allocation strategy based on a reinforcement learning algorithm; a two-stage resource scheduling mode is adopted, non-preemptive resource allocation of priority guarantee is implemented in an event triggering stage, and an optimization strategy of continuous adjustment is deployed in a steady-state stage. According to the method, the resource utilization efficiency and the service quality in a high-concurrency scene are remarkably improved, the method is compatible with an O-RAN standard interface, and the method is suitable for high-reliability and low-delay communication scenes such as smart cities and industrial internet.

The invention relates to the technical field of advertisement putting and intelligent decision making, in particular to a favorite advertisement putting system for predicting the advertisement click rate, which comprises a context awareness intelligent adaptation unit and a deep enhancement decision making unit. By means of deep semantic analysis and situational inference engine processing, interest keywords are extracted by constructing a specific model, user situational portraits are constructed in combination with multi-source data, a preliminary advertisement set is screened by matching with an advertisement material library, a multi-agent architecture is constructed by a deep reinforcement decision unit, a master agent performs overall planning, slave agents are responsible for different advertisement types, and a user can perform multi-agent interaction. A multi-dimensional reward function system is designed, each agent collects feedback data such as operation of a user on an advertisement page, a reward value is calculated according to the feedback data, a strategy network is updated, a main agent integrates information to optimize an overall advertisement pushing strategy, accurate pushing of advertisements is achieved, and the advertisement click rate and the putting effect are effectively improved.

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention discloses a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

The invention discloses a speech recognition and natural language processing integration method and system, and the method comprises the steps: carrying out the multi-modal data fusion according to a speech signal of a user, context text information and environment sensor data, and obtaining a fused multi-modal feature vector; inputting the multi-modal feature vector into a speech recognition model based on an adaptive deep neural network, and performing speech-to-text processing to obtain text output; inputting the text output into a semantic analysis model based on a graph neural network, and performing context semantic analysis and user intention recognition to obtain semantic representation of the user intention and a confidence score of the semantic representation; and according to the semantic representation and the confidence score thereof, dynamically adjusting parameters of the speech recognition model and the semantic analysis model by using a feedback optimization technology based on reinforcement learning, and generating a model optimization strategy. According to the embodiment of the invention, the accuracy of speech recognition and the semantic comprehension capability of natural language processing can be improved.

The invention provides an insurance customer service method and system based on natural language processing, and relates to the technical field of language processing, and the method comprises the steps: obtaining multi-source insurance customer service historical session data, carrying out the text preprocessing, and constructing an initial insurance knowledge graph based on deep semantic analysis; performing knowledge value evaluation on the newly added session content and dynamically updating a knowledge graph; a question and answer matching model of a two-way gating circulation unit network is adopted to carry out multi-hop type association matching, a path priority score is calculated through a path state evaluation model, and intelligent insurance customer service is achieved. The real-time performance and the accuracy of the insurance knowledge graph are improved, and the accuracy and the intelligent level of question and answer matching are enhanced.

The invention relates to the technical field of medical informatization, and discloses a method for generating a medical record report based on a doctor-patient dialogue, and the method comprises the following steps: multi-modal data collection: collecting the multi-modal data of the doctor-patient dialogue, including voice data, video data and environment data; multi-modal data preprocessing: preprocessing the collected data, including noise reduction and feature extraction of voice signals, key point extraction of video data and time synchronization of the multi-modal data; and semantic analysis: converting the voice signal into a text by using a voice recognition technology, carrying out semantic analysis on the text, and extracting key information. Through multi-modal data acquisition, semantic analysis, emotion modeling and knowledge graph reasoning technologies, the comprehensiveness, logicality and dynamic correction ability of medical record generation are improved, meanwhile, the defects of patient psychological state analysis in the prior art are overcome, information orderliness and doctor decision-making efficiency are remarkably improved through generation of the visual medical record report, and the medical record report generation method is suitable for popularization and application. And powerful support is provided for precise medical treatment.

The invention relates to the field of image processing, and discloses an intelligent drawing checking method and system based on a multi-modal large language model, and the method comprises the steps: obtaining a to-be-checked target engineering design drawing and a to-be-checked task description; generating a global overview drawing based on the target engineering design drawing; performing global semantic analysis according to the global overview map and the review task description through a multi-modal large language model, and generating a global semantic analysis result and to-be-reviewed local area proposal information; cutting a local image from the design drawing; performing element identification analysis on the local image through a multi-modal large language model to obtain local structured information; and performing information fusion processing on the local structured information and the global semantic analysis result, generating complete drawing information, performing compliance verification and defect positioning on the complete drawing information and the structured specification knowledge base, and generating a review report. According to the method, intelligent review of the power grid engineering design drawing can be realized, the review efficiency and accuracy are improved, and meanwhile, the resource consumption is reduced.

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

The invention discloses a data processing method and system based on a bidding and tendering management platform. The method comprises the following steps: obtaining a bidding and tendering data set and constructing an initial supplier set; dynamically screening the initial supplier set based on a preset qualification verification rule to generate a compliance supplier set; performing multi-dimensional semantic analysis on bidding document contents in the compliance supplier set to generate a risk assessment report; and generating a recommended supplier sequence based on the risk assessment report and the bid invitation party demand information. The method has the following advantages and effects: semantic mapping and dynamic weight distribution among data are realized; based on dynamic rules and real-time data updating, response delay caused by manual intervention is reduced; through confidence scoring, a quotation rationality model and performance matching degree calculation, a quantifiable risk assessment report is generated, and subjective deviation is reduced; and the recommendation logic is dynamically adjusted in combination with the customized demand of the bid invitation party, and the intelligent level and the result credibility of the bid invitation and tendering process are improved.

The invention discloses an intelligent customer service dialogue model construction method and application, and relates to the technical field of artificial intelligence. Comprising an intention recognition module, a knowledge graph management module, a multi-modal interaction module and a reply generation optimization module. Wherein the intention recognition module judges the intention category consulted by a client; performing semantic analysis on a text input by a client by adopting a natural language processing technology based on deep learning in combination with word vector representation and a bidirectional long-short term memory network; and training is performed through the tagged corpus, and feature expressions of different business intentions are autonomously learned, so that questions of clients are accurately classified into a predefined intention category system. The designed intelligent customer service template covers multiple personality dimensions, the score can be dynamically adjusted according to the feedback of the customer, and then the template most conforming to the personality requirement of the customer is recommended for the customer; therefore, the interactive experience between the customer and the customer service can be enhanced, and the recognition of the customer to the service is improved.

The embodiment of the invention discloses a prompt injection defense method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the preliminary detection of an input text, and obtaining a preliminary detection result; performing semantic analysis on the input text to generate a semantic understanding analysis report; obtaining current operation parameters of the target large language model; according to the current operation parameters, obtaining a behavior pattern analysis result of the target large language model; obtaining a current defense strategy of the target large language model according to the preliminary detection result, the semantic comprehension analysis report and the behavior pattern analysis result; according to the method, the input text and the running state of the large language model are closely combined, so that the prompt injection attack suffered by the large model can be accurately detected, the large model can be efficiently prevented from being hostile attacked, and the corresponding defense strategy can be formulated in a complex attack scene; more effective defense is realized, and the robustness of the whole defense system is improved.

The invention discloses an AI-based animation split script automatic generation and visual preview method and system, and the method comprises the following steps: 1, receiving a natural language script text inputted by a user, the natural language script text comprising scene description, role action, dialogue and shot indication information; step 2, performing semantic analysis and structured analysis on the script text based on a natural language processing technology, and identifying and extracting key narrative elements; by introducing an artificial intelligence technology, end-to-end automatic generation and interactive optimization from a character script to a dynamic split rehearsal video are realized, the system can deeply understand scenes, actions, role emotions and shot languages in the script, corresponding visual elements are automatically matched and generated, and the dynamic split rehearsal effect is improved. And the timeline and the rhythm conforming to the film and television grammar are constructed, so that the efficiency and the consistency of the split creation are greatly improved, and the professional threshold and the manufacturing cost are reduced.

The invention provides an RAG knowledge base construction method and system based on hierarchical semantic indexes. The method belongs to the cross technical field of artificial intelligence and information retrieval. The method comprises the following steps: performing multi-level semantic analysis on an input original document set to generate document semantic hierarchical structure data; constructing a hierarchical semantic index tree based on the document semantic hierarchical structure data; performing dynamic knowledge graph initialization according to the hierarchical semantic index tree to generate an initial dynamic cognitive graph; and collecting real-time interaction data through a user feedback interface and a new data access module, and performing incremental updating on the initial dynamic cognitive map to form a knowledge representation system supporting life cycle evolution. Through multi-level semantic analysis and construction of a hierarchical semantic index tree (HSIT), deep semantic analysis can be performed on an original document set, the context integrity of knowledge is ensured, structured storage is realized, the knowledge can be expressed and stored more accurately, and information loss or semantic ambiguity is avoided.

The embodiment of the invention provides a project research and development data key information processing method and device. A research and development content recognition system and a self-adaptive research and development knowledge graph are constructed. Unified processing and time sequence alignment of text, voice and image contents are realized through multi-modal information decomposition and fusion. Semantic completion and error correction are carried out based on research and development of a semantic analysis model, a knowledge graph structure is dynamically constructed and optimized, and a structured document with a traceability relation is generated. The system adopts a deep neural network model to extract research and development key information, constructs a multi-level document framework, and realizes intelligent conversion from research and development data to a project application document. According to the method, the defects of the traditional technology in the aspects of multi-modal information processing and knowledge structure optimization are effectively overcome, and the research and development data management and project declaration efficiency is remarkably improved.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

The invention provides an LLM technology-based real-time voice conversation method and system applied to an operation and maintenance platform, and relates to the technical field of data processing, the method comprises the following steps: receiving user voice input, carrying out noise reduction preprocessing, and converting a voice signal into text data; semantic analysis is conducted on the text data through an LLMAgent module, the intention of a user instruction is recognized, and a task execution plan is generated based on the intention; decomposing the task execution plan into a basic operation sequence which can be executed by a Unity platform, and determining the priority of task execution; executing the basic operation sequence in a Unity environment, synchronizing an operation state in real time and feeding back an execution result; user interaction data and system performance data are collected, and LLM model parameters and decision strategies are optimized. The technical problems of poor voice interaction effect, large response delay, low accuracy and the like in the existing Unity platform are solved.

The invention relates to the technical field of multi-source heterogeneous data fusion of territorial space planning, and particularly discloses a territorial space planning multi-source heterogeneous data intelligent integration system based on semantic analysis. The system comprises a data acquisition and identification module, a semantic density balance degree analysis module, a boundary fuzzy infectivity analysis module, a comprehensive reliability evaluation module and a semantic calibration feedback module. Starting from overall balance and local boundary stability of semantic distribution, semantic conflicts and leakage problems in multi-source data are accurately recognized, reliability evaluation is performed in combination with a deep learning model, semantic dynamic calibration is realized by using knowledge graph reasoning and transfer learning, and a closed-loop feedback mechanism is formed.

The invention discloses a retrieval generation method and system based on multi-agent collaboration, a terminal and a medium, and relates to the field of artificial intelligence. Performing semantic analysis on the input word embedding converted by the natural language query instruction through a query analysis agent, and determining a semantic intention vector; performing reinforcement learning and meta learning on the semantic intention vector through a strategy construction agent, and determining a retrieval strategy; performing semantic enhancement on the semantic intention vector according to knowledge graph node embedding to obtain a semantic enhancement vector; determining a data channel according to the semantic enhancement vector, a retrieval strategy and a real-time system load, and calling the data channel for retrieval to obtain candidate documents; and generating a target answer according to each candidate document based on an adaptive reflection feedback mechanism in combination with an auto-encoder and a generative adversarial network. The problems that the prior art depends on a fixed retrieval strategy, has limitation when facing complex query, multi-round interaction and cross-modal data fusion, is easily interfered by noise and is not accurate enough in semantic matching are effectively solved.

The invention relates to the field of standard comparison analysis, in particular to a method for automatically identifying differences of domestic and overseas standard files. A method for automatically identifying differences of standard files at home and abroad comprises the following four steps of S1, standard file analysis and information extraction, S2, semantic understanding and information integration, S3, standard difference comparison, S4, compliance analysis and suggestion, and in combination with a rule engine and a natural language generation technology, evaluating difference influences and outputting adjustment suggestions. According to the method, comprehensive analysis of the standard file is realized through multi-modal integration and structured processing, and a knowledge base with cross-language and format is constructed; clause association reasoning is realized through deep semantic analysis and knowledge graph construction; according to the method, accurate difference recognition is achieved through hybrid embedding and a dynamic threshold strategy, the accuracy and efficiency of standard comparison are remarkably improved, the manual intervention cost is reduced, and an efficient solution is provided for standard system analysis in international trade and technical cooperation.

The invention relates to an automatic modeling agent system based on an MCP protocol and a large model, and the system comprises a multi-modal input interface module which is used for receiving, analyzing and standardizing modeling instructions or intention descriptions inputted by a user through different media to form structured feature representations; the semantic understanding central module is used for performing semantic analysis on the structured feature representation by using a pre-trained large language model and outputting a user modeling intention; the MCP instruction compiler module is used for converting a user modeling intention into an MCP instruction sequence following an MCP protocol; and the modeling execution engine module is used for analyzing and automatically executing the MCP instruction sequence to complete modeling of the target three-dimensional model. According to the method, three-dimensional modeling can be completed without knowing professional modeling terms and concepts by a user, and the modeling automation and intelligence level is greatly improved.

The invention discloses a multi-path thinking chain reasoning generation method based on fine-grained knowledge retrieval, which comprises the following steps of: performing deep semantic analysis on a user question by adopting a large language model, and extracting a relationship between entities contained in a question text to generate a triple set; the triple is dynamically divided into different confidence sets by setting high and low confidence thresholds; constructing a thinking chain framework by using a predefined path to obtain an initial reasoning path set; optimizing the initial reasoning path, and sampling an optimized reasoning path set by using Top-k scoring to obtain a reasoning path set with high semantic relevancy; dividing the reasoning path set into a plurality of reasoning path groups with complementary internal path information to obtain a plurality of candidate answers; and adopting a majority voting mechanism to select a plurality of candidate answers, and finally obtaining consistent answers. According to the method, the reasoning accuracy and interpretability can be remarkably improved, and the reliability and robustness of a final conclusion are greatly improved.

The invention discloses an intelligent information screening method and system for a human resource system, and relates to the technical field of human resource systems. The problem that a traditional matching method is difficult to deeply mine semantic association and cannot accurately measure the matching degree of candidates and posts is solved. According to the method, through multi-step intelligent data processing and optimization, the candidate screening efficiency and accuracy are remarkably improved, a semantic analysis technology is adopted to construct a multi-layer topic tree and an emotion analysis model, and deep semantic matching of candidate resumes and position requirements is achieved; meanwhile, candidates are intelligently screened by using a bidirectional semantic matching network, the screening effect is continuously optimized by mining potential high-matching candidates and historical data feedback, bidirectional recommendation is realized through a block chain communication link, the security and efficiency of data transmission are guaranteed, complex enterprise recruitment requirements can be effectively adapted, the recruitment period is shortened, and the recruitment efficiency is improved. The man-post matching accuracy is improved, and the human resource management cost is greatly reduced.

The present application provides a shopping guide method and system based on a large language model, and the method is applied to the technical field of artificial intelligence. The method comprises: acquiring a training shopping guide requirement and a carried content enhancement configuration (S201); on the basis of the content enhancement configuration, generating a training prompt describing the training shopping guide requirement (S202); using a large language model to be trained to perform semantic analysis on the training prompt so as to determine a plurality of application interfaces required for responding to the training shopping guide requirement (S203); on the basis of a preset database, performing screening and summarization processing on returned results of the plurality of application interfaces to obtain predicted shopping guide information (S204); on the basis of the predicted shopping guide information and the training shopping guide requirement, performing parameter adjustment on the large language model to obtain a trained large language model (S205); and, when shopping guiding is conducted, on the basis of a prompt corresponding to a shopping guide requirement inputted by a user end, guiding the trained large language model to generate and output target shopping guide information. According to the method, target shopping guide information that is more in line with a user's natural language can be provided for the user end.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses an intelligent correction method and device based on semantic analysis, equipment and a medium. The method comprises the following steps: extracting a structured document content from an explanation data source, extracting an auxiliary explanation text corresponding to each unit in the structured document content from the explanation data source, carrying out semantic understanding processing on the structured document content and the auxiliary explanation texts by utilizing a semantic analysis module to generate a semantic analysis result, and executing a correction operation through an intelligent correction module based on the semantic analysis result. And after the correction operation is completed, the structured document content and the auxiliary explanation text are input into a format reconstruction module, and a specification-combined document is generated and output. According to the method, semantic understanding is carried out by combining structured document content and the auxiliary explanation text, and an automatic and standardized document compliance processing flow is realized based on intelligent correction and format reconstruction driven by a compliance semantic analysis result.